Performance Improvement Design of Bluetooth Low Energy-Based Wireless Indoor Positioning Systems

Abstract

The systematic design of wireless indoor positioning systems can offer another essential approach to achieving the required performance objectives aside from using suitable location determination algorithms. This manuscript investigates Bluetooth Low Energy- (BLE-) based wireless indoor positioning systems and how adjusting the system design parameters can affect their location determination performance. Without placing emphasis on sophisticated location determination algorithms, this work provides guidelines for how a system designer can control the balance among multiple positioning performance metrics. For example, a balance between the number of installed reference nodes and the accuracy performance can be chosen to control deployment costs, such as the installation expense, infrastructure expense, and installation time. To demonstrate our baseline study, we compare three different designs of BLE wireless indoor positioning system that utilize location determination algorithms based on proximity, trilateration, and scene analysis. These designs are also compared over two different building sizes, which are medium and large. The design model and performance analysis data were based on our actual implementation of the hardware and software system for a BLE wireless indoor positioning system. Specifically, the received signal strength indication data were collected from our prototype reference nodes. The findings from our study indicated that a proximity-based system can only provide fair location accuracy performance (average error distance of 5 m to 7 m) making it unsuitable for applications that require high accuracy. For medium location accuracy performance (average error distance of 3 m to 5 m), the trilateration-based system can achieve the highest efficiency in terms of number of installed reference nodes over the accuracy. The trilateration-based system can reduce the number of installed reference nodes by 154% to achieve the same level of accuracy as the scene analysis-based system. For good location accuracy performance (average error distance ≤ 3 m), the scene analysis-based system yields the highest scalability performance in terms of installed reference nodes. The scene analysis-based system can reduce the number of reference nodes by 40% and 113% to achieve the same accuracy performance when compared with trilateration and proximity-based systems, respectively. Finally, the validation results from the actual installation of Bluetooth-based indoor positioning systems confirmed that our proposed framework can help the system designers to achieve the required performance goal.